Statistics

“One”

Mohamed Ahmed Hefny, MD.

• Before you can successfully analyze a data set, you should understand the differences among different types of data or variables.

• The type of analysis or test that you will run depends largely on the type of data you have.

• These terms may be a bit confusing because of overlapping or similar definitions.

Data

Quantitative (Metric)

Qualitative(Categoric

al)

Continuous Discrete OrdinalNominal

Categorical data

Nominal categorical data

• These data are nominal categorical data (or just nominal data for short).

• The data are ‘nominal’ because it usually relates to named things, such as sex, occupation, blood type, or ethnicity. It is particularly not numeric.

• It is ‘categorical’ because we allocate each value to a specific category. i.e., we allocate each Male & Female in a class

Notice two things about this data, which is typical of all nominal data:• The data do not have any units of measurement.• The ordering of the categories is arbitrary. In other

words, the categories cannot be ordered in any meaningful way.

We could just as easily have written the number of males and females in any order suitable.

Ordinal categorical data

• It is ordinal because the values can be meaningfully ordered, and it is categorical because each value is assigned to a specific category. i.e. Glasgow Coma Scale

• Notice two things about this variable, which is typical of all ordinal variables

1. The data do not have any units of measurement (so the same as that for nominal variables).

2. The ordering of the categories is not arbitrary, as it is with nominal variables.

1. Because ordinal data are not real numbers, it is not appropriate to apply any of the rules of basic arithmetic to this sort of data. You should not add, subtract, multiply or divide ordinal values.

2. This limitation has marked implications for the sorts of analyses that we can do with such data.

3. It should be noted that ordinal data are almost always integer, that is, they have whole number values.

Metric data

Discrete metric data

• Discrete metric data comes from counting. Counting is a form of measurement – hence the name ‘metric’.

• The data is ‘discrete’ because the values are in discrete (separate) steps; for example, 0, 1, 2, 3 and so on.

• Other examples of discrete metric data would include number of deaths, number of pressure sores, number of angina attacks, number of hospital visits and so on.

• Metric discrete variables can be counted and can have units of measurement – ‘numbers of things’

• They produce data which are real numbers and are always integers (i.e. whole numbers).

Continuous metric data

Metric continuous data result from measurement and they have units of measurement.• The data are real numbers. • Because metric data values are real numbers,

you can apply all of the usual mathematical operations to them.

• This opens up a much wider range of analytic possibilities than is possible with either nominal or ordinal data.

• i.e. weight and blood pressure

How can I tell what type of variable I’m dealing with?

Has the data got units ? (Includes number of things)

Can the data be put

in meaningful order?

Do the data come from

counting or measuring ?

Categoricalnominal

Categoricalordinal

Continuousmetric

Discretemetric

No Yes

No Yes Counti

ngMeasurin

g

Thank You